Is there a method for creating a large data set from a smaller one?
I have a data set of anthropometric variables (e.g. stature, leg length, arm length and so on)
So I have 7 variables and 1774 samples. From this data I would like to create a much larger data set of the same 7 variables but with 100,000 samples.
I know there is an "almost" linear relationship between stature and the other body-measurements but I want random variation in my data.
I am using Python, and I have looked at PCA, multivariate analysis and stuff like that. Here's an illustration of my problem:
Edit: Here is additional information. The data contains 7 variables that describes the dimensions of 1774 people. I want to create dimensions of random 100,000 people who are different from each other but still remains realistic.
I tried this method, but cannot figure out how or whether it is possible to generate realistic data using this.
I would assume that the variables are normal distributed. I know that variable "2-7 correlates" well with "variable 1" which is stature.